“production data”的搜索结果

为生产功能设计日志、指标、跨服务追踪和告警，记录请求标识与处理起点，并验证实际输出和通知，帮助了解运行状态及故障原因。

• 完善生产功能的监控信息
• 关联不同服务的日志
• 审查告警与响应操作手册

Generate Drizzle database migrations on a fresh Neon branch and review their production safety. Check table sizes, locking order, execution time, and data transfer needs, with load testing for changes affecting busy or large tables.

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• Generate migrations after changing a database schema
• Review locking risks before production deployment
• Rehearse large table changes under load

Plan AI-assisted SEO, from keyword research and competitor comparison pages to content production using data and templates. It also guides content changes for AI search citations, tool selection, quality checks, and ongoing performance tracking.

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• Planning competitor alternative and comparison pages
• Designing page production using data and templates
• Reviewing content for citations in AI Overviews

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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用于规划生产数据库的结构变更和现有数据迁移，整理列与索引的添加、分阶段切换及恢复步骤，帮助在变更时考虑停机时间和数据库锁。

• 为生产数据表添加列或索引
• 分批更新大量数据
• 在不中断应用的情况下替换列

Review Entity Framework Core code and get improvements for database models, queries, saving changes, and migrations, which track database schema changes. Covers performance, security, testing, and how database access code is organized.

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• Review database models and relationships
• Improve slow queries and avoid repeated database calls
• Check migrations before production deployment

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Audit a codebase before production deployment across eight areas, including security, databases, dependencies, and AI usage. Receive findings with file locations, manual checklists, and a release verdict that blocks shipping when critical issues remain.

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• Check an application before its first production release
• Find security and database issues before deployment
• Review backup restoration and rollback readiness

Review plans involving customer data, compliance, or production access. Examine threats, potential damage, detection, incident response, notification requirements, and vendors, then summarize whether the plan can proceed, needs mitigation, or should be blocked.

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• Reviewing a feature that handles customer data
• Preparing for a SOC 2 or ISO 27001 audit
• Assessing a vendor that will access data

Manage which n8n instance an MCP session targets when working across production, staging, teams, or clients. Guides instance switching, checks before credential changes, and recovery from unexpected missing data or ambiguous credential writes.

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• Switching between production and staging
• Checking the target before credential changes
• Investigating unexpected missing data

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.

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• Automate model training and retraining workflows
• Record and compare machine learning experiments
• Build a feature store for model input data

Rehearse Convex schema changes on a deployment seeded with a production snapshot. Populate existing records, verify data and behavior, then repeat the proven sequence in production with explicit approval, keeping the snapshot available for rollback.

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• Rehearse schema changes using a copy of production data
• Populate existing records before requiring a new field
• Verify migrated data and app behavior before production

Build Next.js 14+ applications with App Router, covering pages, server-side processing, data fetching, and caching. The skill also guides performance improvements, search engine metadata, and production deployment to Vercel or a self-hosted environment.

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• Building pages and layouts with App Router
• Implementing form updates with Server Actions
• Configuring data fetching and cache revalidation

Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases. **Trigger when user asks to:** - Test a schema migration before applying it to production - Add, remove, or rename columns safely on a live table - Change a column's data type without downtime - Add or drop indexes, constraints, or foreign keys on large tables - Understand which ALTER TABLE operations lock the table - Roll back a failed migration - Plan a zero-downtime migration strategy - Fork a database to test a migration safely **Keywords:** migration, schema change, ALTER TABLE, add column, drop column, rename column, change type, zero downtime, lock, AccessExclusiveLock, concurrent index, forking, rollback, backfill, deploy Covers: lock-level reference for every common DDL operation, safe migration patterns, fork-based testing, zero-downtime column changes, index creation, constraint addition, backfill strategies, pre/post-migration validation, and rollback planning.

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Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

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Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

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Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.

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在应用上线前查找未完成的代码和临时实现，验证真实数据库及外部API连接，并检查并发性能、身份验证与部署配置，了解上线准备情况。

• 上线前查找未完成的代码
• 验证真实数据库与API集成
• 检查并发访问性能

Set up or review production error monitoring to capture unhandled errors, failed requests, and performance signals. Check event context, release labels, and alert routing so teams receive useful information when problems occur.

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• Setting up error monitoring for a new production app
• Investigating errors that monitoring fails to capture
• Reviewing critical alerts and on-call routing

Implement Python services for Azure Cosmos DB NoSQL with guidance for authentication, data models, database operations, and tests. Separate business logic from storage access, validate data access permissions, and use reusable templates for consistent implementation.

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• Adding Cosmos DB storage to a FastAPI application
• Separating database access from business logic
• Configuring Azure and local emulator authentication

Turn graded execution traces and production logs into model training examples and preferred-versus-rejected response pairs. Select useful records, remove sensitive information, exclude evaluation examples, and preserve links to the original runs.

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• Convert high-scoring successful runs into training examples
• Use expert-corrected failures as training examples
• Pair passing and failing responses to the same task